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Found 6 papers, 5 papers with code
RedPen: Region- and Reason-Annotated Dataset of Unnatural Speech
We present a novel speech dataset, RedPen, with human annotations on unnatural speech regions and their corresponding reasons.
StyLEx: Explaining Style Using Human Lexical Annotations
Large pre-trained language models have achieved impressive results on various style classification tasks, but they often learn spurious domain-specific words to make predictions (Hayati et al., 2021).
DailyTalk: Spoken Dialogue Dataset for Conversational Text-to-Speech
The majority of current Text-to-Speech (TTS) datasets, which are collections of individual utterances, contain few conversational aspects.
SSMix: Saliency-Based Span Mixup for Text Classification
Data augmentation with mixup has shown to be effective on various computer vision tasks.
KLUE: Korean Language Understanding Evaluation
We introduce Korean Language Understanding Evaluation (KLUE) benchmark.
STYLER: Style Factor Modeling with Rapidity and Robustness via Speech Decomposition for Expressive and Controllable Neural Text to Speech
Previous works on neural text-to-speech (TTS) have been addressed on limited speed in training and inference time, robustness for difficult synthesis conditions, expressiveness, and controllability.
